1. Technical Field
The present disclosure relates generally to computer vision, and more particularly, to security screening and long range video surveillance using computer vision.
2. Discussion of Related Art
Security screening systems inspect checked and hand baggage, cargo, containers, passengers, etc. for content, such as, explosives, improvised explosive devices (IEDs), firearms, contraband, drugs, etc. They play a key role in the Homeland Defense/Security strategy for increased safety in airports, air and sea traffic. For instance, since August 2010 the government has mandated 100% air cargo screening, with possible extension to sea cargo. State-of-the-art security screening systems require improvement in a number of aspects. This includes (a) efficient and effective automation for improved throughput and focused operator attention and (b) a systems view and integration of various components in screening, e.g., reconstruction, segmentation, detection, recognition, visualization, standards, platform, etc., to achieve an efficient screening workflow.
A current system for security screening involves two stages. In a first, automated, stage, X-Ray, CT, etc. scan data is obtained and image reconstruction is performed. Resulting images often encode material properties, such as, density or effective atomic number Zeff. Then, pixels or voxels of suspicious density and Zeff are identified, and contiguous regions segmented. Statistics of suspicious regions (e.g., mass, volume, etc.) are computed and compared to critical thresholds. In a second stage, identified suspicious regions are manually verified for occurrence of a threat by the human operator. This strategy is employed in many screening systems developed by various scanner vendors. However, these systems require a large amount of operator supervision, due to the large number of false alarms.
Further, there is an increasing need for fast extraction and review, from real-time and archived surveillance video, of activities involving humans, vehicles, packages or boats. This need has been driven by the rapid expansion of video camera network installations worldwide in response to enhanced site security and safety requirements. The amount of data acquired by such video surveillance devices today far exceeds the operator's capacity to understand its contents and meaningfully search through it. This represents a fundamental bottleneck in the security and safety infrastructure and has prevented video surveillance technology from reaching its full potential.
Automated video analytics modules operating over video surveillance systems provide one means of addressing this problem, by analyzing the contents of the video feed and generating a description of interesting events transpiring in the scene. However, these modules are inadequate to robustly detect human and vehicular activities in video.